Geng Li
Tsinghua University
14 Papers
37 Citations
Geng Li is an academic researcher from Tsinghua University. The author has contributed to research in topics: Doping & Thermoelectric effect. The author has an hindex of 10, co-authored 14 publications.
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Papers
Atom table convolutional neural networks for an accurate prediction of compounds properties
Shuming Zeng,Yinchang Zhao,Geng Li,Ruirui Wang,Xinming Wang,Jun Ni +5 more
- 08 Aug 2019
TL;DR: This work develops an atom table convolutional neural networks that only requires the component information to directly learn the experimental properties from the features constructed by itself, which is valuable for high throughput screening and helpful to understand the underlying physics.
Intrinsic Thermal conductivities of monolayer transition metal dichalcogenides MX 2 (M = Mo, W; X = S, Se, Te)
TL;DR: This work systematically investigated the thermal transport properties of monolayer transition metal dichalcogenides MX2 by combining the first-principles calculations with Boltzmann transport equation, and finds that monolayers WTe2 possesses the lowest lattice thermal conductivity among these six semiconducting materials.
Strain Effect on the Superconductivity in Borophenes
TL;DR: In this article, the effects of strain on the structure stability, electron-phonon coupling, and superconductivity of two types of monolayer borophenes realized in the experiments are systematically investigated within the framework of density functional theory.
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Intrinsic electronic transport and thermoelectric power factor in n-type doped monolayer MoS2
TL;DR: In this article, the electronic transport and thermoelectric properties in n-type doped monolayer MoS2 were investigated by a parameter-free method based on first-principles calculations, electron-phonon coupling and Boltzmann transport equation (BTE).
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Machine Learning-Aided Design of Materials with Target Elastic Properties
TL;DR: In this article, a set of universal descriptors which combine atomic properties with crystal fingerprint are presented to build interpretable models for elastic property prediction, using the well-performed model.
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